The Business Process Management for Healthcare (BPM+ Health) Consortium: motivation, methodology, and deliverables for enabling clinical knowledge interoperability (CKI).

OBJECTIVES: To enhance the Business Process Management (BPM)+ Healthcare language portfolio by incorporating knowledge types not previously covered and to improve the overall effectiveness and expressiveness of the suite to improve Clinical Knowledge Interoperability. METHODS: We used the BPM+ Health and Object Management Group (OMG) standards development methodology to develop new languages, following a gap analysis between existing BPM+ Health languages and clinical practice guideline knowledge types. Proposal requests were developed based on these requirements, and submission teams were formed to respond to them. The resulting proposals were submitted to OMG for ratification. RESULTS: The BPM+ Health family of languages, which initially consisted of the Business Process Model and Notation, Decision Model and Notation, and Case Model and Notation, was expanded by adding 5 new language standards through the OMG. These include Pedigree and Provenance Model and Notation for expressing epistemic knowledge, Knowledge Package Model and Notation for supporting packaging knowledge, Shared Data Model and Notation for expressing ontic knowledge, Party Model and Notation for representing entities and organizations, and Specification Common Elements, a language providing a standard abstract and reusable library that underpins the 4 new languages. DISCUSSION AND CONCLUSION: In this effort, we adopted a strategy of separation of concerns to promote a portfolio of domain-agnostic, independent, but integrated domain-specific languages for authoring medical knowledge. This strategy is a practical and effective approach to expressing complex medical knowledge. These new domain-specific languages offer various knowledge-type options for clinical knowledge authors to choose from without potentially adding unnecessary overhead or complexity.

Venous Access: National Guideline and Registry Development (VANGUARD): Advancing Patient-Centered Venous Access Care Through the Development of a National Coordinated Registry Network.

There are over 8 million central venous access devices inserted each year, many in patients with chronic conditions who rely on central access for life-preserving therapies. Central venous access device-related complications can be life-threatening and add tens of billions of dollars to health care costs, while their incidence is most likely grossly mis- or underreported by medical institutions. In this communication, we review the challenges that impair retention, exchange, and analysis of data necessary for a meaningful understanding of critical events and outcomes in this clinical domain. The difficulty is not only with data extraction and harmonization from electronic health records, national surveillance systems, or other health information repositories where data might be stored. The problem is that reliable and appropriate data are not recorded, or falsely recorded, at least in part because policy, payment, penalties, proprietary concerns, and workflow burdens discourage completeness and accuracy. We provide a roadmap for the development of health care information systems and infrastructure that address these challenges, framed within the context of research studies that build a framework of standardized terminology, decision support, data capture, and information exchange necessary for the task. This roadmap is embedded in a broader Coordinated Registry Network Learning Community, and facilitated by the Medical Device Epidemiology Network, a Public-Private Partnership sponsored by the US Food and Drug Administration, with the scope of advancing methods, national and international infrastructure, and partnerships needed for the evaluation of medical devices throughout their total life cycle.

A method for structuring complex clinical knowledge and its representational formalisms to support composite knowledge interoperability in healthcare.

INTRODUCTION: The use and interoperability of clinical knowledge starts with the quality of the formalism utilized to express medical expertise. However, a crucial challenge is that existing formalisms are often suboptimal, lacking the fidelity to represent complex knowledge thoroughly and concisely. Often this leads to difficulties when seeking to unambiguously capture, share, and implement the knowledge for care improvement in clinical information systems used by providers and patients. OBJECTIVES: To provide a systematic method to address some of the complexities of knowledge composition and interoperability related to standards-based representational formalisms of medical knowledge. METHODS: Several cross-industry (Healthcare, Linguistics, System Engineering, Standards Development, and Knowledge Engineering) frameworks were synthesized into a proposed reference knowledge framework. The framework utilizes IEEE 42010, the MetaObject Facility, the Semantic Triangle, an Ontology Framework, and the Domain and Comprehensibility Appropriateness criteria. The steps taken were: 1) identify foundational cross-industry frameworks, 2) select architecture description method, 3) define life cycle viewpoints, 4) define representation and knowledge viewpoints, 5) define relationships between neighboring viewpoints, and 6) establish characteristic definitions of the relationships between components. System engineering principles applied included separation of concerns, cohesion, and loose coupling. RESULTS: A "Multilayer Metamodel for Representation and Knowledge" (M*R/K) reference framework was defined. It provides a standard vocabulary for organizing and articulating medical knowledge curation perspectives, concepts, and relationships across the artifacts created during the life cycle of language creation, authoring medical knowledge, and knowledge implementation in clinical information systems such as electronic health records (EHR). CONCLUSION: M*R/K provides a systematic means to address some of the complexities of knowledge composition and interoperability related to medical knowledge representations used in diverse standards. The framework may be used to guide the development, assessment, and coordinated use of knowledge representation formalisms. M*R/K could promote the alignment and aggregated use of distinct domain-specific languages in composite knowledge artifacts such as clinical practice guidelines (CPGs).

Utilization of BPM+ Health for the Representation of Clinical Knowledge: A Framework for the Expression and Assessment of Clinical Practice Guidelines (CPG) Utilizing Existing and Emerging Object Management Group (OMG) Standards.

Clinical Practice Guidelines (CPG), meant to express best practices in healthcare, are commonly presented as narrative documents communicating care processes, decision making, and clinical case knowledge. However, these narratives in and of themselves lack the specificity and conciseness in their use of language to unambiguously express quality clinical recommendations. This impacts the confidence of clinicians, uptake, and implementation of the guidance. As important as the quality of the clinical knowledge articulated, is the quality of the language(s) and methods used to express the recommendations. In this paper, we propose the BPM+ family of modeling languages as a potential solution to this challenge. We present a formalized process and framework for translating CPGs into a standardized BPM+ model. Further, we discuss the features and characteristics of modeling languages that underpin the quality in expressing clinical recommendations. Using an existing CPG, we defined a systematic series of steps to deconstruct the CPG into knowledge constituents, assign CPG knowledge constituents to BPM+ elements, and re-assemble the parts into a clear, precise, and executable model. Limitations of both the CPG and the current BPM+ languages are discussed.

Modeling a Clinical Pathway for Contraception.

BACKGROUND: The Centers for Disease Control and Prevention (CDC) produced a 72-page document titled "U.S. Selective Practice Recommendations for Contraceptive Use" in 2016. This document contains the medical eligibility criteria (MEC) for contraceptive initiation or continuation based on a patient's current health status. Notations such as Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) might be useful to model such recommendations. OBJECTIVE: Our objective was to use BPMN and DMN to model and standardize the processes and decisions involved in initiating birth control according to the CDC's MEC for birth control initiation. This model could then be incorporated into an electronic health records system or other digital platform. METHODS: Medical terminology, processes, and decisions were modeled in coordination with the CDC to ensure correctness. Challenges in terminology bindings were identified and categorized. RESULTS: A model was successfully produced. Integration of clearly defined data elements proved to be the biggest challenge. CONCLUSION: BPMN and DMN have strengths and weaknesses when modeling medical processes; however, they can be used to successfully create models for clinical pathways.

Interoperability of Medication Classification Systems: Lessons Learned Mapping Established Pharmacologic Classes (EPCs) to SNOMED CT.

Interoperability among medication classification systems is known to be limited. We investigated the mapping of the Established Pharmacologic Classes (EPCs) to SNOMED CT. We compared lexical and instance-based methods to an expert-reviewed reference standard to evaluate contributions of these methods. Of the 543 EPCs, 284 had an equivalent SNOMED CT class, 205 were more specific, and 54 could not be mapped. Precision, recall, and F1 score were 0.416, 0.620, and 0.498 for lexical mapping and 0.616, 0.504, and 0.554 for instance-based mapping. Each automatic method has strengths, weaknesses, and unique contributions in mapping between medication classification systems. In our experience, it was beneficial to consider the mapping provided by both automated methods for identifying potential matches, gaps, inconsistencies, and opportunities for quality improvement between classifications. However, manual review by subject matter experts is still needed to select the most relevant mappings.